Unpaid Care And Domestic Work By Region

Box Whisker

Photo by Sebastian Santacruz on Unsplash

Everybody wants to save the earth; no one wants to help mom do the dishes…
— P.J. O’Rourke

Load the data

Have women’s and men’s unpaid care and domestic work converged in high-income countries?

# Load data
url_root <- "https://raw.githubusercontent.com/UN-AVT/kamino-source/main/sources/0-shared/data/"
url_file <- "unpaid-care-and-domestic-work-by-region/unpaid-care-and-domestic-work-by-region.csv"
url <- paste0(url_root, url_file)

df <- read.csv(url, header = TRUE, stringsAsFactors = TRUE, encoding="UTF-8")
df

Wrangle

clean column names

# Clean column name
df_wrangle <- df %>% dplyr::rename(Country=Country, Region=SDG.Region, Ratio=Ratio..W.M.)
df_wrangle

Wrangle

calculate and filter by regional ratios to create labels

# Calculate Max and Min ratio for each Region
regionRatio <- df_wrangle %>%
  group_by(Region) %>%
  dplyr::mutate(
    MaxByR = max(Ratio, na.rm = T),
    MinByR = min(Ratio, na.rm = T)
  ) %>%
  dplyr::arrange(Region)

regionRatio

# Filter the Max and Min value only, below countries will be our labels
country_label_max <- filter(regionRatio, MaxByR == Ratio)
country_label_min <- filter(regionRatio, MinByR == Ratio & Country != "Norway")

Plot

region and ratio

theme_opts <- theme(
    text = element_text(family = "inconsolata", size = 16), 
    plot.title = element_text(color = "black", size = 16, face = "bold"),
    plot.subtitle = element_text(color = "black", size = 12),
    plot.caption = element_text(color = "#555555", size = 11),
    axis.title.x = element_blank(),
    # axis.title.y = element_blank(),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, face = "bold"),
    panel.border = element_blank(),
    panel.background = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.y = element_blank(),
    #panel.grid.major.y = element_blank(),
    legend.position='none'
)

region_labels <- c("Australia and\nNew Zealand",
                   "Central and\nSouthern Asia",
                   "Eastern and\nSouth-Eastern Asia",
                   "Europe and\nNorth America", 
                   "Latin America and\nthe Caribbean", 
                   "Northern Africa and\nWestern Asia", 
                   "Oceania excl.\nAustralia and\nNew Zealand",
                   "Sub-Saharan Africa"
                   )

v1 <- ggplot(df_wrangle, aes(x = Region, y = Ratio)) + 
  geom_boxplot(fill = "#ACC8ED", colour = "black") +
  geom_dotplot(binaxis='y', stackdir='center', dotsize = 0.3, fill="#A6609C", color="#A6609C") +
  scale_x_discrete(labels = region_labels) +
  scale_y_continuous(breaks = seq(0,11,1), labels = seq(0,11,1)) +
  labs( 
       title = "Ratio of female-to-male time spent",
       subtitle = "on unpaid care and domestic work by region",
       x = "", 
       y = "ratio of female-to-male") +
  theme_bw() +
  theme_opts

girafe(ggobj = v1, width_svg = 16, height_svg = 9,
       options = list(opts_sizing(rescale = TRUE, width = 0.8)))

Plot

with country labels

v2 <- v1 +
  geom_text(data = country_label_max, aes(label = Country, y = (Ratio + 0.2)), vjust = 0.0, hjust = 0.5) +
  geom_text(data = country_label_min, aes(label = Country, y = (Ratio - 0.2)), vjust = 1.0, hjust = 0.5)


girafe(ggobj = v2, width_svg = 16, height_svg = 9,
       options = list(opts_sizing(rescale = TRUE, width = 0.8)))

References

The citations and data sources used for this case

Narrative and Data Source: UN Women, Progress of the Worlds Women, GO

@misc{unwomen_2018_making,
  author = {UN Women},
  title = {MAKING EVERY WOMAN AND GIRL COUNT 2018 ANNUAL REPORT IMPLEMENTATION PHASE},
  url = {https://data.unwomen.org/sites/default/files/documents/women%20count%202018%20annual%20report.pdf},
  urldate = {2021-06-22},
  year = {2018}
}